Optical transport core networks are used to transmit voice, data, video, multimedia, and other forms of communication services. In the past, different communication services (e.g., voice and data) were delivered over core networks by distinct dedicated transport networks that were separately managed and operated. However, the dedicated transport networks were complex and required excessive manpower to operate. Moreover, the use of dedicated transport networks to deliver different services resulted in inefficient utilization of network resources.
More recently, packet-based, converged, multi-service networks (P-CMSNs) have been introduced for transporting mixed types of services. Compared with the costs associated with dedicated transport networks, P-CMSNs afford service providers savings in both capital and operating expenses.
For P-CMSNs to successfully support mixed communication services, several basic requirements should be satisfied. For example, the mixed services may include different classes of service, each of which has a specific set of quality-of-service (QoS) requirements. The P-CMSNs should provide adequate levels of QoS for the different classes of service. For example, adequate QoS levels should be provided to allow service providers to guarantee QoS for premium services. Maintaining QoS requirements usually entails the use of techniques designed to provide adequate monitoring and control of QoS parameters such as end-to-end latency, end-to-end jitter, and packet loss associated with transporting communication services.
Unfortunately, P-CMSNs include characteristics that form obstacles to the successful adherence to QoS requirements. For example, packet-based networks conventionally adopt highly distributed dynamic routing mechanisms, which often and unexpectedly redistribute traffic loads over the packet-based networks, based on unscheduled network events, such as congestions or faults. This causes routing instability and makes traffic loads and patterns very difficult to predict in packet-based networks. In addition, existing techniques for predicting and characterizing network traffic load congestions are not suited for modeling aggregate packet traffic from multiple sources. Because of the unpredictable routing nature of P-CMSNs, and the lack of suitable traffic engineering tools, service providers cannot reliably guarantee QoS levels for premium services carried by P-CMSNs, which results in lost revenues, customer dissatisfaction, or both.
Because maintaining QoS is a challenge to the implementation of a P-CMSN, attempts have been made to circumvent the QoS issue. For example, intelligent queuing mechanisms have been implemented at egress ports of P-CMSN packet routers to manage the flow of packet data over the networks. While these queuing mechanisms can accommodate routine fluctuations of traffic demand as well as many unexpected network events, the complexity of the mechanisms renders them difficult to implement and manage. Because the queuing mechanisms should be implemented at the egress port for every router in the P-CMSN in order to be effective, the complexity of implementing and managing the mechanisms makes them impractical to implement over wide area networks. Moreover, there is no guarantee that the queuing mechanisms can prevent congestion caused by unforeseen network events.
Another approach for circumventing the QoS issue can be referred to as “over-engineering” the network. This term refers to a technique used to control the utilization rates of links of the network. In general, when the utilization of a link exceeds a certain threshold, the link will become congested and QoS will begin to deteriorate. To prevent the link utilization rate from exceeding the threshold, excess bandwidth is permanently provisioned so that even peak traffic loads are not likely to exceed the threshold. However, this approach is not practical or cost-effective, especially for wide area networks, because the extra bandwidth (which may be 30% to 50% of the total bandwidth) is not used to generate revenue during non-peak traffic flow patterns. Consequently, the total costs of services are increased.
The above-described approaches for circumventing the QoS issue, when used either alone or in combination, have not been able to overcome their inherent limitations. Consequently, conventional techniques for implementing P-CMSNs do not reliably, efficiently, or cost-effectively maintain and deliver desired QoS levels for communication services transmitted over the P-CMSNs.